System, Method, Software and Data Structure for Independent Prediction of Attitudinal and Message Responsiveness, and Preferences For Communication Media, Channel, Timing, Frequency, and Sequences of Communications, Using an Integrated Data Repository

ABSTRACT

The present invention provides a system, method, software and data structure for independently predicting attitudinal and message responsiveness, using a plurality of attitudinal or other identification classifications and a plurality of message content or version classifications, for a selected population of a plurality of entities, such as individuals or households, represented in a data repository. The plurality of predictive attitudinal (or identification) classifications and plurality of predictive message content (ore version) classifications have been determined using a plurality of predictive models developed from a sample population and applied to a reference population represented in the data repository, such as attitudinal, behavioral, or demographic models. For each predictive attitudinal (or identification) classification, at least one predominant predictive message content or version classification is independently determined. The exemplary embodiments also provide, for each predictive attitudinal classification, corresponding information concerning predominant communication media (or channel) types, predominant communication timing, predominant communication frequency, and predominant communication sequencing.

CROSS-REFERENCE TO A RELATED APPLICATION

This application is a divisional of and claims priority to co-pendingU.S. patent application Ser. No. 10/881,436, filed Jun. 30, 2004,entitled “System, Method and Software for Prediction of Attitudinal andMessage Responsiveness”, inventors Marc Christian Fanelli et al.,commonly assigned herewith, the entire contents of which areincorporated by reference herein with the same full force and effect asif set forth in their entirety herein, with priority claimed for allcommonly disclosed subject matter.

FIELD OF THE INVENTION

The present invention relates, in general, to database managementsystems and, more particularly, to a system, method and software forindependently predicting attitudinal and message responsiveness, andpreferences for communication media, channel, timing, frequency, andsequences of communications, using an integrated data repository.

BACKGROUND OF THE INVENTION

Business and consumer records are typically contained in databases andother forms of data repositories. Typical databases may contain recordssuch as demographic data, customer data, marketing data, name andaddress information, observed and self-reported lifestyle and otherbehavioral data, consumer data, public record information, realty andproperty tax information, summarized automotive statistics, summarizedfinancial data, census data, and so on. Virtually any type ofinformation may be contained in any such database. One such highlyinclusive database, containing much of the above-mentioned types of datafor approximately 98% of U.S. individuals and living units (households),is the Experian INSOURCE® database.

Various database applications have been directed to attempts to utilizethe wide array of information contained in such databases for marketingand analytical purposes. For example, demographic data may be appendedto customer records, to identify the demographic composition of a set ofcustomers, followed by marketing directed toward people having similardemographic characteristics.

These database applications, in their various forms, attempt tounderstand and access distinct customer and prospect groups, and thensend the right message to the right individual, household, living unitor other target audience. Typically, all of the individuals and/orhouseholds contained in the corresponding database are segmented intogroups which share distinct demographic, lifestyle, and consumerbehavior characteristics. In other applications, following suchsegmentation, consumer attitudes and motivations are assumed andattributed to those individuals/households within each such segment orcluster. The number of segments utilized varies widely by application.

In addition, in these various database marketing applications, consumerattitudes and preferred marketing message themes or types are generallyassumed and assigned to a segment, without any independent empiricalresearch and analysis. As a consequence, once a population is segmented,any further analysis of the population based on preferred messagingthemes does not, in fact, add any additional, independent information,and merely reiterates the underlying message theme assumptions of anygiven segment.

The resulting data, moreover, may have a large degree of uncertainty,may or may not be accurate, and may or may not be actionable. Forexample, the attitudes, motivations and behaviors attributed or assignedto each segment may not be accurate and may not be based on factual,empirical research. Such attitudes, motivations and behaviors may or maynot actually reflect representative attitudes found in a particularcustomer database.

The diminished accuracy of current marketing methods is furtherunderscored by comparatively low response rates, such as 1-2% responsefrom a target audience for direct mail marketing. Other methods andsystems are required to appropriately target and motivate the remainderof the target audience, and to determine potentially new andunderdeveloped target audiences. In addition, new methods and systemsare required to maximize marketing returns, by not overly saturating thetarget audience with excessive and ineffective communications, andinstead to appropriately communicate with the target audience using theaudience's preferred methods and times of communication.

As a consequence, a need remains for a predictive methodology andsystem, for accurate prediction of attitudes, motivations and behaviors,which may be utilized for marketing applications. Such a method andsystem should be empirically-based, such as based on actual attitudinal,behavioral or demographic research and other information from apopulation sample, and further should provide accurate modeling topredict and extrapolate such attitudinal or other information to alarger or entire population. Such a method and system should provideinformation concerning preferred message themes or message contentindependently from any population grouping, segmentation or clusteringprocess. In addition, such a method and system should be actionable,providing not only audience attitudinal information and preferredmessage content, but also preferred communication channel information orother preferred communication media, preferred frequency ofcommunication or other contact, and communication timing information.

SUMMARY OF THE INVENTION

The present invention provides a system, method and software forindependently predicting a plurality of first, message contentclassifications and a plurality of second, attitudinal classifications,for a selected population of individuals, households, living units orother groupings of people represented in a data repository, such as aselected population of customers or prospects represented in a databaseor data files. In addition, the system, method and software of theinvention, depending on the selected embodiment, also determinepreferences for communication channel or other media forms,communication timing, frequency of communication, and/or sequences oftypes of communication.

The illustrated, exemplary embodiments of the present invention areempirically-based, using actual attitudinal research and otherinformation from a population sample. Other types of research or datamay also be utilized, such as transactional data, demographic data,marketing research data, or other types of survey information. With thisempirical basis, the invention provides accurate modeling to predict andextrapolate such attitudinal, behavioral, demographic or otherinformation to a larger reference population, thereby providing foraccurate prediction of attitudes, motivations and behaviors, which maybe utilized for marketing applications, for example. The exemplaryembodiments of the invention further provide information concerningpreferred message themes or message content independently from anypopulation grouping, segmentation or clustering process. In addition,the exemplary embodiments of the invention provide actionable results,providing not only audience attitudinal information and preferredmessage content, but also preferred communication channel information,communication media, communication frequency, and communication timingand sequencing information.

The power of the invention cannot be overstated. As indicated above,prior art methods have focused on finding “who”, namely, thoseindividuals or households to whom marketers should direct theircommunications. None of these prior art methods provide, independentlyof the selection of “who”, determination of the “what” of thecommunication, such as preferred content or versions of marketinginformation. None of these prior art methods provide independentinformation on the “when” of the communication, such as the customer'sor prospect's preferred time of day to receive communications. None ofthese prior art methods provide independent information on the “how” ofthe communication, such as the customer's or prospect's preferred mediumor channel for communication, such as direct mail, telephone, electronicmail (email), broadcast media, print media, and so on. Lastly, none ofthese prior art methods provide independent information on the frequency(how often) and sequencing (ordering) of the communications, based onpreferences, such as print media for a first number of times, followedby direct mail for a second number of times, followed by email, forexample.

More specifically, in exemplary embodiments, the present inventionprovides a method, system and software for independently predicting botha plurality of first predictive classification, referred to as messagecontent classifications, and a plurality of second predictiveclassifications, referred to as attitudinal or other behavioralclassifications, for a selected population of a plurality ofindividuals, households, living units or other groupings of persons, as“entities”, represented in a data repository. As used herein, anyreference to “entity” or “entities” should be understood to mean andinclude any individual, household, living unit, group or potentialgrouping of one or more people, whether related or unrelated,individually or collectively, however defined or demarcated, such as ahousehold, a living unit, a geographic unit, or any other grouping ofindividuals for whom or which data may be maintained, generally at agranular or atomic level, in a database.

In the exemplary embodiments, empirical attitudinal research andpredictive attitudinal classifications are illustrated as examples, andshould be understood to mean and include other forms of research andclassifications, such as behavioral or demographic classificationsformed from corresponding empirical research, such as correspondingbehavioral or demographic survey research, for example.

The various exemplary method, system and software embodiments of theinvention, perform the following:

-   -   First, for each entity (e.g., individual or household) of the        plurality of entities of the selected population, appending from        the data repository a corresponding predictive attitudinal        classification of a plurality of predictive attitudinal        classifications, and a corresponding plurality of predictive        message content classifications, with the corresponding        predictive attitudinal classification and corresponding        plurality of predictive message content classifications having        been determined using a plurality of predictive (attitudinal)        models developed from a sample population and applied to a        reference population represented in the data repository.    -   Second, for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications, determining        a penetration index of the selected population compared to the        reference population.    -   Third, for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications,        independently determining at least one predominant predictive        message content classification from the appended plurality of        predictive message content classifications of the plurality of        individuals of the selected population having the corresponding        predictive attitudinal classification of the plurality of        predictive attitudinal classifications.

In addition, depending upon the selected embodiment, for each entity(e.g., individual or household) of the plurality of entities of theselected population, the various embodiments optionally provide forappending from the data repository a corresponding predictivecommunication media (or other channel) classification of a plurality ofpredictive communication media classifications, a correspondingpredictive communication timing classification of a plurality ofpredictive communication timing classifications, a correspondingpredictive frequency of communication classification of a plurality ofpredictive communication frequency classifications, and a correspondingpredictive sequence of communications of a plurality of predictivecommunication sequence classifications, with these classificationshaving been determined from information stored in the data repository.

Typically, the plurality of predictive communication mediaclassifications comprises at least two of the following communicationmedia (equivalently referred to as communication channels): electronicmail, internet, direct mail, telecommunication, broadcast media (such asradio, television, cable, satellite), video media, optical media (DVD,CD), print media (such as newspapers, magazines), electronic media (suchas web sites and electronic forms of newspapers, magazines), and publicdisplay media (such as signage, billboards, multimedia displays).Depending upon the selected embodiment, the plurality of communicationmedia and channel classifications may be more or less specific, such asfurther subdividing print and electronic media channels into newspaper,weekly magazines, monthly magazines, journals, business reports, andfurther into their print, internet, email or electronic versions. Inaddition, various forms of broadcast media may have any of a pluralityof forms, such as cable, satellite, television and radio frequencytransmission, internet, etc. Also typically, the plurality of predictivecommunication timing classifications comprises at least two of thefollowing communication timing classifications: morning, afternoon,evening, night, weekday, weekend, any time (no preference), and none.The plurality of predictive communication frequency classificationstypically comprises at least two of the following frequency ofcommunication classifications: daily, weekly, biweekly, monthly,semi-monthly, bimonthly, annually, semi-annually, and none. Lastly, theplurality of communication sequences are highly varied and may include,for example, print communications, followed by electroniccommunications.

In the various embodiments, the plurality of predictive message contentclassifications are or have been determined by:

-   -   first, developing a plurality of empirical attitudinal factors        based on a factor analysis of an attitudinal survey of a sample        population;    -   second, using each empirical attitudinal factor of the plurality        of empirical attitudinal factors, scoring each participant of        the attitudinal survey to create a corresponding plurality of        empirical attitudinal factor scores;    -   third, using a plurality of selected variables from the data        repository as independent variables, and using the corresponding        plurality of empirical attitudinal factor scores as dependent        variables, performing a regression analysis to create the        plurality of predictive attitudinal models;    -   fourth, using each predictive attitudinal model of the plurality        of predictive attitudinal models, scoring the plurality of        entities represented in the data repository, as the reference        population, to create the plurality of predictive message        content classifications; and    -   fifth, independently determining the plurality of predictive        attitudinal classifications by a cluster analysis of the        plurality of predictive message content classifications of each        entity of the plurality of entities represented in the data        repository.        As indicated above, in lieu of or in addition to the attitudinal        research and predictive attitudinal classifications, other types        of research and corresponding classifications may also be        formed, such as behavioral, demographic, and transactional.

The invention also provides for determining core, niche and growthattitudinal classifications, as follows:

-   -   determining one or more core attitudinal classifications by        selecting, from the plurality of predictive attitudinal        classifications, at least one predictive attitudinal        classification having a comparatively greater (e.g., average or        above average) penetration index and having a comparatively        greater proportion of a selected population;    -   determining one or more niche attitudinal classifications by        selecting, from the plurality of predictive attitudinal        classifications, at least one predictive attitudinal        classification having a comparatively greater penetration index        and having a comparatively lesser proportion of the reference        population; and    -   determining one or more growth attitudinal classifications by        selecting, from the plurality of predictive attitudinal        classifications, at least one predictive attitudinal        classification having a comparatively lesser (e.g., below        average) penetration index and having a comparatively greater        proportion of the reference population.

In yet another aspect of the invention, the exemplary embodimentsprovide a method and system for independently predicting communicationresponsiveness of a selected population of a plurality of entitiesrepresented in a data repository. The method comprises: (a) for eachentity of the plurality of entities of the selected population,appending from the data repository a corresponding predictiveidentification classification of a plurality of predictiveidentification classifications, wherein the plurality of predictiveidentification classifications designate a plurality of entitiesaccording to a selected property; (b) for each entity of the pluralityof entities of the selected population in a corresponding predictiveidentification classification, appending at least one correspondingpredictive message version classification of a plurality of predictivemessage version classifications, the plurality of predictiveidentification classifications and the plurality of predictive messageversion classifications having been determined from a plurality ofpredictive models developed from a sample population and applied to areference population represented in the data repository; and (c) foreach predictive identification classification of the plurality ofpredictive identification classifications, independently determining atleast one predominant predictive message version classification from thecorresponding, appended predictive message version classifications ofthe plurality of entities of the selected population of the predictiveidentification classification. The selected property is derived from atleast one of the following: attitudinal characteristics, behavioralcharacteristics, demographic characteristics, geographiccharacteristics, financial characteristics, or transactionalcharacteristics.

In yet another aspect of the invention, the exemplary embodimentsprovide a data structure for independently predicting communicationresponsiveness of a selected population of a plurality of entitiesrepresented in a data repository. Such a data structure may be stored ina database, transmitted electronically, or stored in a tangible medium.The data structure comprises: a first field having a plurality ofpredictive identification classifications, wherein the plurality ofpredictive identification classifications designate a plurality ofentities according to a selected property; and a second field having,for each predictive identification classification of the first field, atleast one predominant predictive message version classification of aplurality of predictive message version classifications, the pluralityof predictive identification classifications and the plurality ofpredictive message version classifications having been determined from aplurality of predictive models developed from a sample population andapplied to a reference population represented in the data repository.

The data structure may also include a third field having, for eachpredictive identification classification of the first field, at leastone predominant predictive communication media classification of aplurality of predictive communication media classifications; a fourthfield having, for each predictive identification classification of thefirst field, at least one predominant predictive communication timingclassification of a plurality of predictive communication timingclassifications; a fifth field having, for each predictiveidentification classification of the first field, at least onepredominant predictive communication frequency classification of aplurality of predictive communication frequency classifications; a sixthfield having, for each predictive identification classification of thefirst field, at least one predominant predictive communicationsequencing classification of a plurality of predictive communicationsequencing classifications; and a seventh field having a penetrationindex for each predictive identification classification of the pluralityof predictive identification classifications. As indicated above, theselected property is derived from at least one of the following:attitudinal characteristics, behavioral characteristics, demographiccharacteristics, geographic characteristics, financial characteristics,or transactional characteristics.

In yet another aspect of the invention, the exemplary embodimentsprovide a method for independently predicting communication mediaresponsiveness of a selected population of a plurality of entitiesrepresented in a data repository, comprising: (a) for each entity of theplurality of entities of the selected population, appending from thedata repository a corresponding predictive identification classificationof a plurality of predictive identification classifications, wherein theplurality of predictive identification classifications designate aplurality of entities according to a selected property; and (b) for eachpredictive identification classification of the plurality of predictiveidentification classifications, independently determining at least onepredominant predictive communication media classification of a pluralityof predictive communication media classifications.

In other embodiments, instead of step (b) above, the exemplary methodprovides for each predictive identification classification of theplurality of predictive identification classifications, independentlydetermining at least one predominant predictive communication timingclassification of a plurality of predictive communication timingclassifications, or for independently determining at least onepredominant predictive communication frequency classification of aplurality of predictive communication frequency classifications.

These and additional embodiments are discussed in greater detail below.Numerous other advantages and features of the present invention willbecome readily apparent from the following detailed description of theinvention and the embodiments thereof, from the claims and from theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will bemore readily appreciated upon reference to the following disclosure whenconsidered in conjunction with the accompanying drawings and exampleswhich form a portion of the specification, in which:

FIG. 1 is a block diagram illustrating first and second exemplary systemembodiments in accordance with the present invention.

FIG. 2 is a block diagram illustrating an exemplary integrated datarepository in accordance with the present invention.

FIG. 3 (divided into FIGS. 3A and 3B and collectively referred to asFIG. 3), is a flow diagram illustrating an exemplary method fordetermination of predictive attitudinal classifications and predictivemessage content classifications using a data repository in accordancewith the present invention.

FIG. 4 (divided into FIGS. 4A, 4B and 4C and collectively referred to asFIG. 4), is a flow diagram illustrating an exemplary method ofindependently predicting a plurality of attitudinal classifications, aplurality of message content classifications, and other predictiveinformation, of a selected population using a data repository inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

While the present invention is susceptible of embodiment in manydifferent forms, there are shown in the drawings and will be describedherein in detail specific examples and embodiments thereof, with theunderstanding that the present disclosure is to be considered as anexemplification of the principles of the invention and is not intendedto limit the invention to the specific examples and embodimentsillustrated.

As indicated above, the present invention provides a system, method andsoftware for independently predicting a plurality of attitudinalclassifications and a plurality of message content classifications, fora selected population of individuals, households or other living units(“entities”) represented in a data repository, such as a database. Theembodiments of the present invention provide a predictive methodology,system and software, for accurate prediction of attitudes, motivationsand behaviors, which may be utilized for marketing, research,assessment, and other applications. The embodiments of the invention areempirically-based upon actual attitudinal research and other informationfrom a population sample, and provide accurate modeling to predict andextrapolate such attitudinal information to a larger referencepopulation. The embodiments of the invention further provide informationconcerning preferred message themes or message content independentlyfrom any population grouping, segmentation or clustering process. Inaddition, the embodiments of the invention provide actionable results,providing not only audience attitudinal information and preferredmessage content, but also preferred communication and media channelinformation, communication frequency, and communication timing andsequence information.

FIG. 1 is a block diagram illustrating first exemplary system embodiment110 and second exemplary system embodiment 150 in accordance with thepresent invention. As illustrated in FIG. 1, the first exemplary systemembodiment 110 is a computer system embodiment (e.g., a mainframecomputer), comprising an input and output (I/O) interface 105, one ormore processors 115, and a memory 120 storing a database (or datarepository) 100A. The memory 120 may be external, such as an externalmagnetic disk, tape, or optical drive. The second system 150, such as anopen or network system, comprises a data repository (or database) 100B(also embodied in a form of memory, discussed below), a databasemanagement server 140, and/or an application server 125. A “datarepository”, “database”, and “data warehouse”, as used herein, areconsidered interchangeable, and may be relational, object-oriented,object-relational, or use files or flat files, or any combinations ofthe above. Both database 100A and 100B are instantiations of a database100, discussed in greater detail below.

In the exemplary embodiments of system 150, the database managementserver 140 and the application server 125 may be implemented together,such as implemented within the application server 125. Either or both ofthe database management server 140 and the application server 125 areconnected or coupled (or couplable) to the data repository (database)100B, for full duplex communication, such as for database queries,database file or record transfers, database updates, and other forms ofdatabase communication. In the second system embodiment 150, thedatabase management server 140 and/or the application server 125 performthe methodology of the invention utilizing a correspondingly programmedor configured processor as discussed below (not separately illustrated),such as a processor 115 illustrated for system 110, in conjunction witha database 100 (such as database 100B).

Typically, the databases 100A and 100B are ODBC-compliant (Open DatabaseConnectivity), although this is not required for the present invention.The first system 110 and second system 150 may also be coupled to or maybe part of a local area network (“LAN”) 130 or, not separatelyillustrated, a wide area network (“WAN”), such as for full duplexcommunication with a plurality of computers (or other terminals) 135,also for database queries, database file or record transfers, databaseupdates, and other forms of database communication. The LAN 130communication capability provides for the first system 110 and secondsystem 150 to be accessible for local access to the databases 100A and100B, such as for large file transfers or other batch processing,discussed in greater detail below. In addition, the first system 110 mayalso be directly accessible (185), such as for loading of records (e.g.,magnetic tape records or other media) for batch processing.

The first system 110 and second system 150 may also be included withinor coupled to a larger data communication network 180, through network(or web) server 160, for full duplex communication with remote devices,such as a remote Internet or other network server 170 and remotecomputer (or other terminal) 175. Such remote communication capabilityprovides for the first system 110 and second system 150 to be accessiblefor on-line functionality, discussed in greater detail below, such asfor web-based access, using any of the prior art protocols, such ashypertext transfer protocol (HTTP) or other Internet Protocol (“IP”)forms of communication for data, voice or multimedia.

The data repository (or database) 100, illustrated as databases 100A and100B, may be embodied in any number of forms, including within any datastorage medium, memory device or other storage device, such as amagnetic hard drive, an optical drive, a magnetic disk or tape drive,other machine-readable storage or memory media such as a floppy disk, aCDROM, a CD-RW, DVD or other optical memory, a memory integrated circuit(“IC”), or memory portion of an integrated circuit (such as the residentmemory within a processor IC), including without limitation RAM, FLASH,DRAM, SRAM, MRAM, FeRAM, ROM, EPROM or EPROM, or any other type ofmemory, storage medium, or data storage apparatus or circuit, which isknown or which becomes known, depending upon the selected embodiment.

In the first system 110, the I/O interface may be implemented as knownor may become known in the art. The first system 110 and second system150 further include one or more processors, such as processor 115illustrated for first system 110. As the term processor is used herein,these implementations may include use of a single integrated circuit(“IC”), or may include use of a plurality of integrated circuits orother components connected, arranged or grouped together, such asmicroprocessors, digital signal processors (“DSPs”), custom ICs,application specific integrated circuits (“ASICs”), field programmablegate arrays (“FPGAs”), adaptive computing ICs, associated memory (suchas RAM and ROM), and other ICs and components. As a consequence, as usedherein, the term processor should be understood to equivalently mean andinclude a single IC, or arrangement of custom ICs, ASICs, processors,microprocessors, controllers, FPGAs, adaptive computing ICs, or someother grouping of integrated circuits which perform the functionsdiscussed below, with associated memory, such as microprocessor memoryor additional RAM, DRAM, SRAM, MRAM, ROM, EPROM or EPROM. A processor(such as processor 115), with its associated memory, may be adapted orconfigured (via programming, FPGA interconnection, or hard-wiring) toperform the methodology of the invention, as discussed above and asfurther discussed below. For example, the methodology may be programmedand stored, in a processor with its associated memory (and/or memory120) and other equivalent components, as a set of program instructions(or equivalent configuration or other program) for subsequent executionwhen the processor is operative (i.e., powered on and functioning).Equivalently, when the first system 110 and second system 150 mayimplemented in whole or part as FPGAs, custom ICs and/or ASICs, theFPGAs, custom ICs or ASICs also may be designed, configured and/orhard-wired to implement the methodology of the invention. For example,the first system 110 and second system 150 may implemented as anarrangement of microprocessors, DSPs and/or ASICs, collectively referredto as a “processor”, which are respectively programmed, designed,adapted or configured to implement the methodology of the invention, inconjunction with a database 100.

The application server 125, database management server 140, and thesystem 110 may be implemented using any form of server, computer orother computational device as known or may become known in the art, suchas a server or other computing device having a processor,microprocessor, controller, digital signal processor (“DSP”), adaptivecomputing circuit, or other integrated circuit programmed or configuredto perform the methodology of the present invention, such as a processor115, as discussed in greater detail below.

FIG. 2 is a block diagram illustrating an exemplary data repository (ordatabase) 100 in accordance with the present invention. As mentionedabove, “data repository” as used herein, is considered interchangeablewith “database”, and may be relational, object-oriented, orobject-relational, or utilize any other database structure, inaccordance with a selected embodiment. The database 100 may beintegrated, namely, that the information resides within a singular,co-located or otherwise centralized database structure or schema, or maybe a distributed database, with information distributed between andamong a plurality of databases, some of which may be remotely locatedfrom the other databases. From another point of view, the database 100may be considered integrated in that a plurality of different tables ortypes of tables, objects or relations are included within the database100, such as including an attitudinal classification table 240 with theother illustrated tables discussed below. While generally not includedwithin the database 100 (as potentially private client data), optionallyone or more copies of a selected population data (file, table ordatabase) 270, such as a client customer databases, client customer flatfiles, or client master databases, may also be utilized. (Use of anytype of data repository, whether an integrated database, anon-integrated database, or any otherwise distributed or non-distributeddatabase structures or schemas, are within the scope of the presentinvention. While referred to as tables, it should be understood that thetables illustrated in the database 100 of FIG. 2 are to be construedbroadly, to mean and include relations, objects, object relations,multidimensional relations, cubes, flat files, or other similar orequivalent database constructs.) In addition, while a plurality ofrelations (or connections) between and among the various tables areillustrated in FIG. 2, it should be understood that in any selectedembodiment, a greater or fewer number of relations, connections,cross-references, keys, or indices may be utilized, all within the scopeof the present invention.

The database 100 generally includes, for example, a name table 205, anaddress table 210, a lifestyle and behavioral data table 215, and ademographic data table 220, and depending upon the selected embodiment,may also include a public record table 225, a census data table 230, asummarized financial data table 235, and other information tables 265.In various embodiments, the name table 205 and address table 210 may becombined as a single table. In the exemplary embodiments, the database100 further includes an attitudinal (or behavioral) classification table240, a message theme table 245, a communication media (or channel) table250, a communication timing table 255, a communication frequency table260, a communication sequence table 285, a response data table 275, anda transactional data table 280, as discussed in greater detail below.While illustrated as separate tables or relations, it should beunderstood that the information contained in such tables may becontained or distributed between or among any number of tables orrelations, depending upon any applicable or selected schema or otherdatabase 100 structure, in any number of equivalent ways, any and all ofwhich being within the scope of the present invention. The datarepository 100 is generally included within a first system 110 and/orsecond system 150, and respectively accessed through the I/O 105 andprocessor 115, or an application server 125 or database managementserver 140, discussed above.

The name table 205 contains all individual, consumer, household, livingunit, group or other entity names, in various forms, variations,abbreviations, and so on, and is also utilized for searching andmatching processes, as discussed below. The address table 210 containsall addresses of individuals, households, living units, groups or otherentities which will be utilized for searching and matching processes, asdiscussed below. In the exemplary embodiment, the lifestyle andbehavioral data table 215 contains lifestyle and behavioral informationfor individuals, households, living units, or other groups or consumers,as “entities”, such as purchase behavior, activity data, and anyself-reported data. The demographic data table 220 contains demographicinformation and possibly geodemographic information for consumers andother individuals, households, living units, or other entities, such asage, gender, race, religion, household composition, income levels,career choice, etc. The public record table 225 contains informationavailable in public records, such as vehicle ownership records, drivingrecords, property ownership records, public proceeding records, securedtransaction records, and so on. The census data table 230 containscensus information, typically available through a government agency. Thesummarized financial data table 235, when included in database 100,typically includes summaries of financial information generally forindividuals or households in a given geographic region (e.g., by postalcode), and could possibly also include bank account information,investment information, securities information, and credit or otherprivate information, when available and to the extent allowable underany applicable regulations or laws.

The transactional data table 280 typically contains informationconcerning purchase history or other transaction history of the variousentities. The response data table 275 contains information typicallyrelated to transactional data, such as for purchases made in response toa particular communications, such as in response to a catalogue, directmail, or a magazine advertisement. The transactional data table 280 andthe response data table 275, for example, may be based upon data fromparticular clients or groups of clients. The information contained inthe lifestyle and behavioral data table 215, the demographic data table220, the public record table 225, the census data table 230, and thesummarized financial data table 235, other information tables 265, andany other available tables, depending upon the selected embodiment, areutilized in the creation of independent variables for the predictiveattitudinal (or behavioral) modeling discussed below. The selectedpopulation data 270, which may be in any of various forms such as atable, a file, a flat file, a relation, a database, or another dataschema, such as a copy of a selected customer database, contains namesor names and addresses of individuals, households, living units, othergroups or entities, such as customers or any other selected ordesignated population, and is utilized to provide predictive attitudinalmarketing information for the selected population, as discussed ingreater detail below. Also optionally included within database 100 areother information tables 265, such as for other demographic information,credit information, and fraud information, when available or authorized.

The attitudinal classification table 240, the message theme table 245,the communication channel table 250, the communication timing table 255,the communication frequency table 260, and the communication sequencetable 285, are generally created, populated or segregated based upon thepredictive attitudinal (or behavioral) modeling discussed below withreference to FIG. 3.

Depending upon the selected database 100 embodiment, a table or index(relation or look-up table) 200 of identifiers or identifications(“IDs”) of a plurality of individuals, consumers, households, livingunits, other groups or entities, may be included within the database100. The identifiers are typically persistent, with every entityassigned at least one ID. In the exemplary embodiments, the ID table 200also provides relations, links or cross-references to a plurality ofother relations or tables, such as, for example, the name table 205, theaddress table 210, the lifestyle and behavioral data table 215, thedemographic data table 220, the public record table 225, the census datatable 230, the summarized financial data table 235, the attitudinalclassification table 240, the message theme table 245, the communicationchannel table 250, the communication timing table 255, the communicationfrequency table 260, the communication sequence table 285, and the otherinformation tables 265. The ID table 200 may also provide relations,links or cross-references to selected population data (file, table ordatabase) 270, depending upon the selected embodiment and the form ofthe data. The ID table 200 may be utilized in the searching and matchingprocesses discussed below, and for other database applications, such asupdating.

FIG. 3 is a flow diagram illustrating an exemplary method fordetermination of first predictive (attitudinal) classifications andsecond predictive (message content) classifications using a datarepository in accordance with the present invention. As indicated above,in accordance with the exemplary embodiments, such first predictiveclassifications are attitudinal and derived using empirical attitudinalresearch; in other embodiments, the first predictive classifications maybe behavioral, demographic, or any combination thereof, and derived fromcorresponding empirical research, such as behavioral or demographicsurvey research. The second predictive classifications are determinedindependently, and in the exemplary embodiments, provide message contentor message theme classifications, which are subsequently utilized todetermine the “what” of a marketing or educational communication, forexample.

The method begins, start step 300, with results of one or moreattitudinal (or behavioral) surveys of individuals, as consumers or asmembers of a living unit (household). As mentioned above, as usedherein, “entity” or “entities” should be understood to mean and includeany individual person, household, living unit, group or potentialgrouping of one or more people, whether related or unrelated,individually or collectively, such as a single individual, a household,a living unit, a geographic unit, or any other grouping of individualsfor which data may be maintained, generally at a granular or atomiclevel in a database. For example, various databases may be maintained inwhich information is stored and available at an individual level, forindividual persons and, in many cases, also maintained at a lessgranular level of living units (households), while in other cases, alsowithin the scope of the present invention, information may be stored andavailable in a database only at a household level, with the predictedattitudinal and messaging classifications then pertaining tocorresponding households (as a larger grouping of one or moreindividuals). In other cases, an individual residing at a first locationmay also be considered to be part of a living unit at a second location,such as a student residing in a college dormitory being considered partof a family household residing at a different location. All suchvariations are within the scope of the present invention and, for easeof reference, any references to an entity or entities means and includesany individual, person, or grouping or collection of persons, howeversuch a grouping may be defined or demarcated.

In the exemplary embodiments, an empirical modeling process is performedin which selected questions from a survey are utilized for obtaininginformation pertinent to consumer purchasing, behaviors, and attitudes(which also may be considered to include various behavioral anddemographic components). Survey questions, data and results areavailable from a wide variety of vendors, publications, and othersources. Survey questions may also be determined based upon the goals ofthe modeling and classification processes. The survey may be conductedand results obtained in any of various forms, such as via telephone,written survey, email survey, mail survey, internet survey, personalinterview, etc. The selected questions from the survey are thensubjected to a factor analysis, step 305, to statistically determinewhich questions are highly related to or among other survey questions,to what degree, and to isolate the significant questions and determinecorresponding factors, step 510, with a selected group of highly relatedsurvey questions forming a selected, corresponding factor. This factoranalysis may also be an iterative process in selected embodiments. Theresulting plurality of empirical attitudinal factors identify attitudes,behaviors and motivations of the survey participants, and identify thesignificant corresponding survey questions. The plurality of empiricalattitudinal factors which are selected depend upon the selected purposesof the classifications discussed below, such as marketing analyses, forexample, and are also dependent upon the selected purposes of the survey(as are the resulting attitudinal and message content classifications,discussed below), and the cultural, sociological, demographic, and othercharacteristics of the sample population of the survey.

In an exemplary embodiment, a plurality of empirical attitudinal factorswere developed as a result of the factor analysis and empiricalattitudinal model determinations of steps 305 and 310. Exemplary factorsfor marketing purposes include, for example, consumer brand loyalty,impulse buying behavior, incentive driven behavior, and so on.Innumerable other factors and corresponding questions will be apparentto those of skill in the art, for other behavioral or demographicmodeling, for example. Corresponding exemplary statements areillustrated below, to which a sample population was asked to agree ordisagree, on a varying scale, from highly agree, somewhat agree,neutral, somewhat disagree, and highly disagree, resulting in anequivalent question format. The sample questions are exemplary, forpurposes of illustration only, and any resulting set of attitudinal,behavioral or demographic models (discussed below) will be empiricallydetermined based upon the purpose of the research survey, the selectedsurvey questions and results, the survey population and culture,followed by factor analysis. Additional survey questions may also beutilized to expand the attitudinal factors utilized. Exemplarystatements, utilized in a representative survey of the presentinvention, include, for example:

If a product is made by a company I trust, I'll buy it even if it isslightly more expensive.

I am willing to pay more for a product that is environmentally safe.

I like to shop around before making a purchase.

I'm always one of the first of my friends to try new products orservices.

I prefer products that offer the latest in new technology.

If I really want something I will buy it on credit rather than wait.

I'd rather receive a sample of a product than a price-off coupon.

Following development of the plurality of empirical attitudinal factorsof steps 305 and 310, all of the survey participants, as a samplepopulation, are scored across each of the empirical attitudinal factors,step 315. For example, various survey participants may have indicatedvarious levels of agreement (or disagreement) with the survey questionsof a particular attitudinal factor, and as such, would be scored high(or low) for that particular attitudinal factor. The scoring process mayalso be computed in probabilistic terms, as a probability of exhibitinga particular attitude. As a consequence, in the exemplary embodiment,each survey participant was scored across the plurality of empiricalattitudinal factors. Also for example, various survey participants mayhave similar scores across a plurality of empirical attitudinal factors,such as high scores for the same first factor, and low scores for thesame second factor.

Following scoring of the survey participants across each of theempirical (attitudinal) factors in step 315, records in the database 100pertaining to the survey participants, as individuals (or entities suchas living units), are searched, and the survey participants are matchedwith their corresponding records within the database 100, step 320,generally utilizing the name table 205 and address table 210. For allentities such as individuals, households, living units, or other groupshaving matching records (i.e., matching individuals/households),demographic, lifestyle, behavioral, and other variables (from thedatabase 100) are appended or linked to each (matching) surveyparticipant, step 325, such as variables from the lifestyle andbehavioral data table 215, the demographic data table 220, the publicrecord table 225, the census data table 230, the summarized financialdata table 235, the transactional data table 280, and the response datatable 275, for example. In the exemplary embodiment, the ExperianINSOURCE® database, as previously described, was utilized as thedatabase 100. Using the appended database variables as independentvariables, and using the empirical attitudinal factor scores asdependent variables, a predictive attitudinal model (or, equivalently,an attitudinally predictive model) is developed for each empiricalattitudinal factor, step 330, thereby generating a correspondingplurality of predictive attitudinal models, one for each empiricalattitudinal factor. In the exemplary embodiment, a logistic regressionanalysis is performed, to identify the database variables (asindependent variables) which are statistically significant predictors ofthe attitude of the corresponding empirical attitudinal factor. Otherstatistical methods, such as multiple linear regression analysis, otherforms of regression analysis, and other forms of modeling andstatistical analysis, are also considered equivalent and within thescope of the present invention. Selection of given database variables isalso a function of the availability of such variables within thedatabase 100, namely, any given database may or may not includevariables available in other databases. Not separately illustrated instep 330, the plurality of attitudinally predictive models may also bevalidated, such as by using a “holdout” (or separate) sample from thesurvey results.

Using the plurality of predictive attitudinal models of step 330, all ormost database entities (or members), namely, all or most individuals,consumers, households, living units, or other groups contained in thedatabase (i.e., contained in the database by having representativeinformation in the database 100), are scored (or otherwise evaluated)across each of the plurality of predictive attitudinal models, step 335.Having been scored/evaluated, these entities (e.g., individuals orhouseholds) then form a reference population, utilized for comparativepurposes discussed below. The results from each such entity (individualor group) being scored or evaluated based on each of the predictiveattitudinal models, in step 335, then form or represent (or otherwisegenerate or determine) a corresponding plurality of predictive messagecontent classifications, also referred to as predictive message themeclassifications, for each such entity (individual or group) representedin the database 100. More specifically, using the plurality ofpredictive attitudinal models, each entity (individual, household,living unit, or other group) represented in the database 100 ispredicted to have a corresponding probability of belonging in or to aclassification associated with a particularattitude/behavior/demographic of interest, in which entities(individuals, households, living units, or other groups) exhibiting thatattitude/behavior/demographic are generally responsive, receptive,attentive, conducive to or motivated by messages or other communicationshaving a particular content or theme. Such content or themes, forexample, to which individuals or groups may be receptive, may bematched, correlated or derived from various information sources, such asinformation contained in the lifestyle and behavioral data table 215,the response data table 275, and the transactional data table 280. As aconsequence, the results from the evaluations using the predictiveattitude/behavior/demographic models provide or form the correspondingpredictive message content classifications for each entity (individual,household, living unit, or other group) of the reference population.

These classifications are referred to as predictive message contentclassifications (or message theme classifications) because, as discussedbelow, they are utilized to predict the message content or messagethemes to which individuals, households, living units, or other groupswithin that classification are likely to be receptive or responsive. Asindicated above, the actual result of the scoring may be a probabilityof exhibiting the attitude in question, or may be a number or percentilewhich can be equivalently translated into such a probability. In theexemplary embodiment, the probability scores were further classifiedinto nine tiers, which were then further utilized to create dichotomousvariables in order to classify the entity (individual or group) aseither exhibiting the attitude of interest or not exhibiting theattitude of interest. For each such entity (individual or group), theresults of step 335, namely, the scores for each of the predictiveattitudinal models and/or each of the resulting predictive messagecontent classifications, are stored in the database, step 340, such asin message theme table or relation 245.

For example, using the scores or evaluations from the plurality ofpredictive attitudinal models for individual (or group) “A”, records maybe stored indicating that “A” has a high probability level of belongingto predictive message content classifications “X”, “Y”, and “Z”, and alow probability level of belonging to each of the remaining predictivemessage content classifications. Alternatively and equivalently, recordsfor individual (or group) “A” may be stored indicating that “A” hascertain scores from the evaluations under each of the predictiveattitudinal models, and belongs to predictive message contentclassifications “X”, “Y”, and “Z” (with dichotomous variables of “1”),and does not belong to each of the remaining predictive message contentclassifications (with dichotomous variables of “0”). As a consequence,depending upon the selected embodiment, all or most entities representedin the database 100 have associated scores for each of the plurality ofpredictive attitudinal models and, correspondingly, a membership (or nomembership), or a degree or probability of membership, in each of thecorresponding plurality of predictive message content (or theme)classifications.

It should be noted that in the exemplary embodiments, the empiricalattitudinal factors and predictive message content classifications havea one-to-one correspondence, and may be very similar. In otherembodiments, there may be more or fewer predictive message contentclassifications compared to empirical attitudinal factors. As indicatedabove, the empirical attitudinal factors are based on the factoranalysis of the survey questions from the sample population and areutilized to develop the predictive attitude/behavior/demographic modelsincorporating the database variables. The predictiveattitude/behavior/demographic models derived from the empirical analysisof the sample population are then extended into the database population,as the reference population. The results from this predictive modelingare then matched or correlated with other database information to createthe corresponding predictive message content classifications. Inaddition, given the appended database variables, various demographic,lifestyle and behavioral characteristics may also be included in or aspart of the descriptions of the predictive message content (or theme)classifications.

In the exemplary embodiments, a plurality of representative, predictivemessage content (or message theme) classifications were developed as aresult of the analysis of step 335. Several examples of predictivemessage content classifications are illustrated below, withcorresponding, exemplary message content guidelines. It will beunderstood in the art that these predictive message contentclassifications are exemplary and for purposes of illustration and notlimitation, and that any resulting set of predictive message contentclassifications will be empirically determined based upon the selectedsurvey purposes; the selected survey questions and results; the surveyor sample population, demographics, socioeconomics and culture; theplurality of predictive attitudinal models; and the extrapolateddatabase population.

Exemplary Predictive Message Content (or Theme) Classifications:

First Exemplary Predictive Message Content (or Theme) Classification:

Exemplary message content guidelines include rewarding and complimentingfor being the first to take advantage of new products and services,highlighting new or cutting edge products or offers, and demonstratingthe prestige of the product/service offered.

Second Exemplary Predictive Message Content (or Theme) Classification:

Exemplary message content guidelines include communicating the strengthand quality of a brand, the importance of relationships and customerservice, emphasizing the quality of a product, emphasizing the number ofyears in business, and integrity and quality awards.

Third Exemplary Predictive Message Content (or Theme) Classification:

Exemplary message content guidelines include a family focus, bonuses,presenting how a product/offer is better than a competitiveproduct/service, price comparison, and value features.

Fourth Exemplary Predictive Message Content (or Theme) Classification:

Exemplary message content guidelines include appealing to altruism,activism, and appreciation for our ecology, the use of naturalingredients, and emphasis on quality with details.

Fifth Exemplary Predictive Message Content (or Theme) Classification:

Exemplary message content guidelines include use of celebrityendorsements and testimonials to emphasize image and style, and use ofincentive gifts.

Sixth Exemplary Predictive Message Content (or Theme) Classification:

Exemplary message content guidelines include demonstrating a fair valueusing a straightforward, logical approach, a masculine emphasis, and useof peer/user comparisons and testimonials.

Referring again to FIG. 3, following scoring of all or most entities(individuals or groups) in the database 100 using the plurality ofpredictive attitudinal models, concomitant assignment of membership (ornon-membership) of the entities (individuals or groups) to thecorresponding plurality of predictive message content classifications instep 335, and storing the resulting information in the database 100 ofstep 340, the exemplary method of the invention performs a cluster orgrouping analysis of all such database members (entities) using either(or both) the corresponding scores from each of the plurality ofpredictive attitudinal models or the resulting assigned membership(s)(or probability of membership(s)) in the predictive message contentclassifications, step 345. This cluster or grouping analysis of step 345not only utilizes the plurality of predictive message contentclassifications (and/or predictive attitudinal models), but alsoutilizes combinations of the various predictive message contentclassifications (or, equivalently, scores from the correspondingpredictive attitudinal models). Any form of cluster or grouping analysismay be utilized, as known or may become known in the field. The resultof this cluster or grouping analysis is a plurality of predictiveattitudinal classifications. For example, those entities that belong orare assigned to the same two predictive message content classifications,or equivalently those entities that scored high in the same twocorresponding predictive attitudinal models, may be clustered or groupedtogether into a first predictive attitudinal classification. Also forexample, those entities who belong in one predictive message contentclassifications and who do not belong in another predictive messagecontent classification may be clustered or grouped together into asecond predictive attitudinal classification. In addition, clusters mayexist for those who belong in only one predictive message contentclassification.

For example, for the plurality of predictive attitudinal classificationsdescribed below: a first exemplary cluster exhibited both a “trendfollowing” attitude and an “impulsive” attitude, but not an “incentivedriven” attitude; and a second exemplary cluster exhibited an“environmentally conscious” attitude, a “brand loyal” attitude, and a“buy American” attitude, but not a “price conscious” attitude.

As a result of the cluster (segmentation or grouping) analysis of step345, a plurality of predictive attitudinal classifications aredeveloped, generally having a greater number of classifications than theplurality of predictive message content classifications, and providinghigher granularity or discrimination among the variousattitudes/behaviors/demographics exhibited among the database referencepopulation. In the exemplary embodiments, using membership ornon-membership in the plurality of predictive message contentclassifications (based on probability scores from the predictiveattitudinal models), clusters were identified where the mean value ofthe dichotomous variable was 0.70 or higher, indicating a segment thathad at least one strong loading.

Following the cluster analysis, in step 350, each entity (individual orgroup) represented in the database is assigned to a predominantpredictive attitudinal (behavioral or demographic) classification, ofthe plurality of predictive attitudinal (behavioral or demographic)classifications, based upon his, her or its highest probability ofexhibiting the attitude(s) (behaviors or demographics) of interest ofthe corresponding classification. This assignment may be determinedequivalently by the entity's scores from the predictive attitudinalmodels and/or the correspondingly determined memberships in one or morepredictive message content classifications. For example, individuals orgroups predicted to exhibit only a single attitude of interest would beassigned to that corresponding predictive attitudinal classification (orcluster), while those exhibiting more than one attitude of interestwould be assigned to a corresponding predictive attitudinalclassification, as a cluster of those particular of attitudes. In theexemplary embodiment, those entities (using dichotomous variables or“all or none” scores for the predictive attitudinal models) not assignedas described above are then re-clustered to identify an optimal segmentor cluster, which may not meet stricter scoring requirements, butnonetheless indicate a predominant, predictive attitudinalclassification. Also in the exemplary embodiments, an entity is assignedto one and only one predictive attitudinal classification; in otherembodiments, multiple predictive attitudinal/behavioral/demographicclassifications may be assigned.

As is the case with the scores from the plurality of predictiveattitudinal models and the corresponding assignments to the plurality ofpredictive message content classifications, such assignments ofpredictive attitudinal classifications are also stored in the database100, step 355, such as in attitudinal classification table or relation240. As a consequence, all or most entities represented in the database100, in accordance with the present invention, have a plurality ofrecords stored in the database 100, namely: (1) either or both theassociated scores (results) for each of the plurality of predictiveattitudinal models and/or, correspondingly and equivalently, amembership (or no membership) or a degree or probability of membershipin the corresponding plurality of predictive message content (or theme)classifications (message theme table 245); and (2) an assignment into apredominant, predictive attitudinal classification (attitudinalclassification table 240). Following step 355, the method ofdetermination of predictive attitudinal classifications and predictivemessage content classifications using a data repository, in accordancewith the present invention, may end, return step 360.

In the exemplary embodiment, a plurality of predictive attitudinalclassifications were developed as a result of the analysis of step 345,and representative examples are illustrated immediately below. For eachsuch exemplary predictive attitudinal classification, correspondingmarketing strategies, lifestyle and interests, demographics, behaviorsand attitudes, and socioeconomic indicators are illustrated, generallyderived from corresponding database variables and other informationavailable in a database 100, as well as syndicated survey research. Itwill be understood in the art that these predictive attitudinalclassifications and their marketing names are exemplary and for purposesof illustration and not limitation, and that any resulting set ofpredictive attitudinal classifications will be empirically determinedbased upon the selected survey questions and results; the surveypopulation, demographics, socioeconomics and culture; the plurality ofpredictive attitudinal models; the extrapolated database population; andthe selected cluster analysis.

Exemplary Predictive Attitudinal (or Behavioral) Classifications:

First Exemplary Predictive Attitudinal (or Behavioral) Classification:

Individuals and households in this first predictive attitudinalclassification stay true to themselves and the brands that they prefer.They are selective with their purchases, and look for well-establishedproducts and services that have demonstrated quality and value.Individuals and households in this first predictive attitudinalclassification are responsive to brand extensions and use coupons on theproducts that they already have an affinity toward. From the database100, their lifestyle and interests include enjoyment of reading andvisits to bookstores; television viewing and preferring informativeprogramming and movie classics; investing wisely and often; maintainingan exercise and fitness regimen; and participation in activities such asgolf, tennis, fishing, and occasional gambling. Also from the database100, their demographics include being established mid-lifers; married,divorced or single; any children are grown and have left home; theytypically own their own homes, and have established residences, usuallyin larger, affluent cities. The behaviors and attitudes of theindividuals and households in this first predictive attitudinalclassification include being ardent catalog shoppers; having apreference for outdoor lifestyle companies; shopping at upscale retailstores; preferring “the real thing” to generic products; visiting thegrocery store frequently with a likelihood of using coupons to save onpreferred brands; and enjoyment of domestic and overseas travel. Theirsocioeconomic indicators include a high income; an above average homevalue; established credit experience with a well-maintained, stablecredit history; an undergraduate degree and some graduate studies;occupations including finance, accounting, engineering and real estate;and they drive luxury vehicles.

Second Exemplary Predictive Attitudinal (or Behavioral) Classification:

Individuals and households in this second predictive attitudinalclassification represent a highly affluent, successful and stableconsumer market, containing established old-wealth and the nouveauriche. Their investments and dividends are as impressive as theirincomes. They aspire to own and use the finest quality brands andservices, and they are willing to pay the extra dollar for the privilegeof living this lifestyle. They enjoy traveling quite extensively, soincentives that provided added benefit in this area are preferable. Theactive lifestyles they lead drive them to utilize all modes ofconvenient communication. The lifestyle and interests of individuals andhouseholds in this second predictive attitudinal classification includea love to travel domestically and overseas, preferring cruises andtours; shopping at mid-level to upscale stores; diverse sports interestsand may be avid golfers; socially involved as club members, theatre andconcert-goers, and with environmental causes. Their demographics includea wide age range, from young to mature adults; largest concentration isestablished and mid-life adults, who are typically married; theirchildren range in age from grade school to high school; they typicallyown their own homes, having well-established residences, usually incomfortable and prosperous neighborhoods, in major and mid-size cities,and in urban city settings. The behaviors and attitudes of theindividuals and households in this second predictive attitudinalclassification include “working to live” rather than “living to work”;they are active, affluent, have an influential lifestyle and arefinancially astute; they make time for family and individual interests,and want the “good life” for their family; they are technology- andinternet-savvy, with frequent web accessing. The socioeconomicindicators include a high income; an above average home value;extensive, established and good credit experience; they have anundergraduate degree with some graduate studies; their occupationsinclude finance, engineering, healthcare, counseling,computer/technology and marketing; they are more likely to leasevehicles than to buy; drive new and used import cars and light trucks,and are drawn to near-luxury, luxury, specialty and SUV models.

Third Exemplary Predictive Attitudinal (or Behavioral) Classification:

Individuals and households in this third predictive attitudinalclassification are dedicated sports fans that enjoy a wide variety ofoutdoor pursuits—from do-it-yourself home improvement projects to scubadiving, and they enjoy their lifestyle. Their independence may make it achallenge to establish relationships with these customers, and theyprefer product samples to coupons to provide immediate proof of theproduct's quality and immediate savings. Their lifestyle and interestsinclude being outdoor enthusiasts; they have no preference for brandname goods over generic brands; they are dedicated sports fans, theyenjoy working on mechanics, home improvement, boating, motorcycles,scuba diving and video games. The demographics of individuals andhouseholds in this third predictive attitudinal classification includemainly being young adults, who are single or divorced, with lowindications of children present in the household; they typically rentinstead of own residences, and live in apartments rather thansingle-family homes, with a wide variety of residential settings, oftentransient or in rural towns. Individuals and households in this thirdpredictive attitudinal classification have behaviors and attitudes suchas liking things to be simple and straightforward, “rough and rugged”,and self-determining. Socioeconomic indicators of individuals andhouseholds in this third predictive attitudinal classification include abelow average income; a slightly below average home value; a newercredit experience with average extension; varied education levels;typically employed in service- and consumer-oriented industries and/ormay be students; they typically drive used, domestic vehicles, andmodels include small to mid-size cars and small- and full-size pickuptrucks.

Fourth Exemplary Predictive Attitudinal (or Behavioral) Classification:

Individuals and households in this fourth predictive attitudinalclassification are conservative, content with the status quo and noteasily swayed. They focus on “hearth and home” for comfort andentertainment, avidly donate to the causes they support, and enjoytimeless activities such as leisure sports, musical performances,gardening, and reading. As consumers, they are motivated to spend moneyon their families, homes and hobbies but are careful to spend it well,making them highly responsive to coupons and discount offers. Theirlifestyle and interests include family-oriented, domestic activitiessuch as home improvement projects, gardening, cooking and entertaining.They are typically passionate donors that support causes such asreligious, political and health issues. They are also devoted book andmagazine lovers and sports enthusiasts. The demographics of theindividuals and households in this fourth predictive attitudinalclassification are that they are mainly seniors and retirees, typicallymarried, whose children have left home (empty nesters). They typicallyown their own homes, usually multi-dwelling units rather thansingle-family homes, and prefer to live in rural towns and small citycommunities. The behaviors and attitudes of the individuals andhouseholds in this fourth predictive attitudinal classification includea relaxed living attitude, with a healthy standard of living, derivingsignificant pleasure from daily activities with family and friends. Theymake the most of their spending and utilize coupons. They like to keepup on interests in music, trivia and collectibles. Socioeconomicindicators for this classification include a low income, with an averageto below average home value; stable, consistent and capable creditexperience; they are typically high school graduates with some college;and primarily are retired. They typically drive domestic used vehiclesthat include mid-range cars and pick-up trucks

The plurality of predictive attitudinal classifications and plurality ofpredictive message content classifications, with additional informationavailable in a database 100 as discussed below, become extraordinarilypowerful tools when applied to a selected population, such as a group ofindividuals represented in a customer database, a prospect database, aclient database, a membership database, an association database, and soon. In the exemplary embodiment, the additional information available inthe database 100 includes, for all or most of the represented (ormatched) individuals, households, living units or other entities: theirpreferred methods of communication and/or communication media(communication media table 250), their preferred times (time of day) ofcommunications (communication timing table 255), their preferredfrequencies of communication (communication frequency table 260), andtheir preferred sequences of communication (communication sequence table285). This additional information may be determined in a wide variety ofways, including self-reported preferences and behaviors, third-partyreported preferences and behaviors (such as transactions, purchases, andactivities), observed preferences and behaviors, and inferredpreferences and behaviors based on modeled data.

FIG. 4 is a flow diagram illustrating an exemplary method ofindependently predicting a plurality of attitudinal classifications anda plurality of message content classifications of a selected populationin accordance with the present invention. In addition, depending uponthe selected embodiment, predicted communication and/or media channels,predicted communication timing, predicted communication frequency, andpredicted communication sequencing, may also be provided as part of themethod illustrated with reference to FIG. 4.

Referring to FIG. 4, the method begins, start step 400, with data aboutor concerning a selected population, such as name and/or name andaddress information from a customer database or file, a customerprospect file or list, or any other identifying data or information ofor for a group of individuals, households, living units, groups or otherentities, for any selected purpose. The database 100 is then searchedand the selected population data is matched with the records of thedatabase 100, step 405, such as matched with the records of theINSOURCE® database. For all records where a match is found in step 405,the method appends, references or links, to each (matched) entity of theselected population, their corresponding (i.e., predominant) predictiveattitudinal classification, and their corresponding predictive messagecontent classification(s), step 410. As discussed above, for each suchentity, their corresponding predictive attitudinal classification isgenerally their predominant attitudinal classification of the pluralityof predictive attitudinal classifications, and their correspondingpredictive message content classifications are generally theirmemberships or probabilities of membership in each of the plurality ofpredictive message content classifications. In the exemplaryembodiments, optionally as part of step 410, the method also appends,references or links the entity's associated information concerningpredicted communication and media channels, predicted communicationtiming, predicted communication frequency, and predicted communicationsequence.

There are a wide variety of alternatives or defaults for non-matchingentities of step 405, including variations depending upon degrees orlevels of matching. Exemplary alternatives include, for non-matchingindividuals or groups, appending and utilizing the average, most commonor mode classifications for a particular geographic region, such as apostal code area. Another alternative includes excluding thosenon-matching individuals or groups from the remainder of the method and,equivalently, the selected population may be considered to be comprisedof the matching entities from step 405. Those of skill in the art willrecognize that the matching step 405 and the appending step 410 may beperformed in a plurality of ways, including use of conditional loops oriterations, with each iteration corresponding to the matching andappending for a given entity, and with iterations continuing until allentities have been matched (or found to not match) and correspondingdata appended.

The method then determines the distribution of the selected populationacross or within each of the predictive attitudinal classifications, toform a corresponding plurality of selected population distributions,step 415. Each selected population distribution is compared to areference distribution for each of the predictive attitudinalclassifications, step 420. Typically, a reference or baselinedistribution is or may be the distribution, across or within each of thepredictive attitudinal classifications, of the larger, often national orregional population represented in the database 100, referred to aboveas the reference population. For example, for a selected population,such as the purchasers of a particular automobile brand, when comparedto a larger regional or national population on a proportional orpercentage basis, that selected population may be comparatively orrelatively over-represented in certain predictive attitudinalclassifications, and that selected population may be comparatively orrelatively under-represented in other predictive attitudinalclassifications.

Based on these comparisons of the distribution of the selectedpopulation with a reference distribution, for each predictiveattitudinal classification of the plurality of predictive attitudinalclassifications, a “penetration” or comparative index (or rate) isdetermined, step 425, with a comparatively greater or higher penetrationindex indicative of a higher proportional concentration of entities ofthe selected population within a given predictive attitudinalclassification compared to the reference distribution, and with acomparatively lower or lesser penetration index indicative of a lowerproportional concentration of entities of the selected population withina given predictive attitudinal classification compared to the referencedistribution. For example, a 15% distribution of the selected populationfor the predictive attitudinal classification of “Q”, when compared toan 8% distribution for the reference population for this same “Q”predictive attitudinal classification, indicates a comparatively higher(or above average) penetration index or rate (a ratio of 1.875) of theselected population in this classification. Similarly, an 11%distribution of the selected population for the predictive attitudinalclassification of “P”, when compared to a 16% distribution for thereference population for this same “P” predictive attitudinalclassification, indicates a comparatively lower (or below average)penetration index or rate (a ratio of 0.6875) of the selected populationin this classification.

In addition, for each predictive attitudinal classification, thereference distribution may be normalized to a particular value, such as100 or 1.0, e.g., a reference distribution of 11% in a first predictiveattitudinal classification may be normalized to 100 and a referencedistribution of 7% in a second predictive attitudinal classification mayalso be normalized to 100. Also for example, for the selectedpopulation, and for a given predictive attitudinal classification, apenetration index of 150 or 1.5 may be utilized to indicate that theselected population has proportionally (or percentage-wise) 50% (or 1.5times) more individuals (households, living units or other groups) inthat given predictive attitudinal classification compared to the largerreference population, such as a national or regional population. Asillustrated above with the various percentage distributions for the “Q”and “R” predictive attitudinal classifications, these comparisons areperformed on a proportional or percentage basis, rather than acomparison of pure or gross numbers, as the selected populationgenerally concerns a considerably smaller total number of individuals(or groups) compared to the reference population represented in thedatabase 100.

As a result of step 425, penetration indices or rates are determined foreach predictive attitudinal classification of the plurality ofpredictive attitudinal classifications, comparing the proportion ordistribution of the selected population in that classification to theproportion or distribution of the reference population in thatclassification. The plurality of predictive attitudinal classificationsare then evaluated by their penetration indices and, depending upon theselected embodiment, are also evaluated based upon the relative (orproportional) reference and selected population sizes within eachpredictive attitudinal classification, step 430. Using the penetrationindices and relative or comparative reference and selected populationsizes of each predictive attitudinal classification, three additionallevels of attitudinal classifications are determined, namely, coreattitudinal classifications, niche attitudinal classifications, andgrowth attitudinal classifications (steps 435, 440, 445). While coredeterminations are usually determined first (to avoid potentialconfusion with niche determinations, as based upon proportions of theselected population in addition to penetration indices), the otherdeterminations may be performed in any order. In other variations,depending upon the selected evaluation algorithm, other determinationorders for core, niche and growth attitudinal classifications may beavailable.

More specifically, in step 435, one or more core attitudinalclassifications are determined by selecting, from the plurality ofpredictive attitudinal classifications, at least one predictiveattitudinal classification having a comparatively greater (e.g., averageor above) penetration index and having a comparatively greaterproportion of the selected population. These core attitudinalclassifications represent predictive attitudinal classifications havingthe largest percentage of the selected population, such as customers,and corresponding, significant market share. With respect to a selectedpopulation of customers of a particular brand, the core attitudinalclassifications represent significant brand appeal to populationsegments exhibiting corresponding behavioral characteristics.

In step 440, one or more niche attitudinal classifications aredetermined by selecting, from the plurality of predictive attitudinalclassifications, at least one predictive attitudinal classificationhaving a comparatively greater (e.g., average or above) penetrationindex and having a comparatively lesser proportion of the referencepopulation. These niche attitudinal classifications represent predictiveattitudinal classifications having a high penetration rate (andcorresponding market share), but a relatively small percentage of thereference population, such as a small percentage of a prospectpopulation.

In step 445, one or more growth attitudinal classifications aredetermined by selecting, from the plurality of predictive attitudinalclassifications, at least one predictive attitudinal classificationhaving a comparatively lesser (e.g., below average) penetration indexand having a comparatively greater proportion of the referencepopulation. These growth attitudinal classifications representpredictive attitudinal classifications having some penetration success,and with the comparatively large percentages of the referencepopulation, such as prospective customers, indicate significantopportunities to increase penetration and add new customers from anotherwise underrepresented group.

To this point in the method of the present invention, considerableattitudinal and behavioral information has been provided, which may beutilized for a wide variety of purposes. Based on empirical modeling,actual attitudes and behaviors of segments of a selected population maybe predicted, using the plurality of predictive attitudinalclassifications. Depending upon selected purposes of the embodiment,additional information may be provided, such as the actual attitudes andbehaviors of individuals or groups in the predictive attitudinalclassifications, including the core, niche and growth classifications.

Additional information is also independently provided in accordance withthe present invention. While a selected population has been predictivelyclassified as exhibiting certain attitudes and behaviors, as “who”segments (such as who among the population are significant customers orprospects), an additional, independent and more fine-grained level ofinformation is also provided, based upon the plurality of predictivemessage content classifications, providing independent “what” segments(such as what content will be most effective). More specifically, theactual members of the selected population, although assigned to apredictive attitudinal classification as a predominant classification,may also exhibit other or different attitudes and behaviors, representedby a probability or membership in one or more predictive message contentclassifications, in addition to those of the predominant predictiveattitudinal classification. As consequence, in step 450, for each of theplurality of predictive attitudinal classifications, the method alsoindependently determines one or more predictive message contentclassifications, based on the predictive message content classificationsof the actual entities (individuals or groups) of the selectedpopulation assigned to that selected predictive attitudinalclassification. For each predictive attitudinal classification, theplurality of predictive message content classifications may also beranked, such as by comparative or relative penetration, proportion ordistribution of a given predictive message content classification forthat predictive attitudinal classification, step 455.

This independent determination of predictive message contentclassifications based upon the actual, selected population (step 450 andoptional ranking step 455) within each predictive attitudinalclassification, may be used to produce (or effectively results in) aninformation matrix or data structure, consisting of the plurality ofpredictive attitudinal classifications (e.g., as rows) and the pluralityof predictive message content classifications (e.g., as columns), bothof which may be further ranked or ordered according to relativedistribution, penetration and/or population size. As a result, not onlymay a selected population be predictively classified or segmentedattitudinally and behaviorally, using the plurality of predictiveattitudinal classifications, they may also be independently andpredictively classified based on content or theme receptivity, using theplurality of predictive message content classifications. Communicationchannel, media, timing, frequency, and sequencing information may alsobe included in such a matrix, e.g., as columns, and is discussed ingreater detail below, as the various fields of a data structure of thepresent invention.

In the exemplary embodiments, with the availability of channel, media,timing, frequency, and sequencing information in the database 100, themethod continues with step 460, in which the predominant communicationchannel and/or media preferences are determined for each predictiveattitudinal classification of the plurality of predictive attitudinalclassifications, based upon the preferred communication channels and/orpreferred media types of the entities (individuals or groups) of theselected population assigned to the given predictive attitudinalclassification, such as email, internet, direct mail, telecommunication,radio (broadcast, cable and satellite), television (network (broadcast),cable or satellite), video (or DVD) media, print media, electronicmedia, visual or other public display media, and depending upon theselected embodiment, the plurality of communication and media channelclassifications may be more or less specific, such as furthersubdividing print and electronic media channels into newspaper, weeklymagazines, monthly magazines, journals, business reports, and furtherinto their print, internet, email or electronic versions. For example,predominant communication channels for a first predictive attitudinalclassification may be, in preferred order, direct mail followed by radiofollowed by email, while predominant communication channels for a secondpredictive attitudinal classification may be, also in preferred order,television followed by telecommunication followed by direct mail.

In step 465, the predominant timing (time of day) preferences forcommunications are determined for each predictive attitudinalclassification of the plurality of predictive attitudinalclassifications, also based upon the communication timing preferences ofthe entities (individuals or groups) of the selected population assignedto the given predictive attitudinal classification. For example,predominant timing preferences for a first predictive attitudinalclassification may be, in preferred order, weekends followed by evening,while predominant timing preferences for a second predictive attitudinalclassification may be, also in preferred order, mornings followed byafternoons. The timing preferences may be further qualified based uponmedia and communication channels, such as predominant timing preferencesbeing evenings for television, and weekends for telecommunications.

In step 470, optionally, the predominant frequency preferences forcommunications are determined for each predictive attitudinalclassification of the plurality of predictive attitudinalclassifications, also based upon the frequency of communicationpreferences of the entities (individuals or groups) of the selectedpopulation assigned to the given predictive attitudinal classification.For example, predominant frequency preferences for a first predictiveattitudinal classification may be, in preferred order, monthly followedby semi-annually, while predominant timing preferences for a secondpredictive attitudinal classification may be, also in preferred order,weekly followed by bi-weekly. The predominant frequency ofcommunications also may be further qualified based on either or bothtiming preferences and media and communication channels, such as nofrequency preference (unlimited) for television communications, and zerofrequency (no communication) for telecommunication channels (e.g.,telephone call, faxes).

In step 475, optionally, the predominant sequencing preferences forcommunications are determined for each predictive attitudinalclassification of the plurality of predictive attitudinalclassifications, also based upon the sequencing of communicationpreferences of the entities (individuals or groups) of the selectedpopulation assigned to the given predictive attitudinal classification.This information may also be incorporated into the matrix discussedabove and the data structure discussed below.

Lastly, this collection of information is output and, in the illustratedexemplary embodiment, also stored in a database, step 480. In theexemplary embodiment, as indicated, a matrix or data structure ofinformation is provided in step 480, indicating the following:

-   -   (1) the plurality of predictive attitudinal classifications,        ordered by core, niche and growth attitudinal classifications        (and corresponding penetration indices);    -   (2) for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications, the        predominant predictive message content classifications of the        selected population assigned to that given predictive        attitudinal classification;    -   (3) for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications, the        predominant communication and/or media channel classifications        of the selected population assigned to that given predictive        attitudinal classification;    -   (4) for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications, the        predominant communication timing classifications of the selected        population assigned to that given predictive attitudinal        classification;    -   (5) as an additional option in the exemplary embodiments, for        each predictive attitudinal classification of the plurality of        predictive attitudinal classifications, the predominant        frequency of communication classifications of the selected        population assigned to that given predictive attitudinal        classification;    -   (6) as an additional option in the exemplary embodiments, for        each predictive attitudinal classification of the plurality of        predictive attitudinal classifications, the predominant        sequencing of communication classifications of the selected        population assigned to that given predictive attitudinal        classification.        Following the output of information in step 480, the method may        end, return step 485.

As indicated above, the system of the present invention generallycomprises a memory storing a data repository (or database) 100 and aprocessor, such as a processor 115 included within a mainframe computerof system 110 or within either (or both) a database management server140 or an application server 125 of system 150. The processor isprogrammed to perform the methodology of the present invention. As aconsequence, the system and method of the present invention may beembodied as software which provides such programming.

More generally, the system, methods and programs of the presentinvention may be embodied in any number of forms, such as within anytype of computer, within a workstation, within an application serversuch as application server 125, within a database management server 140,within a computer network, within an adaptive computing device, orwithin any other form of computing or other system used to create orcontain source code. Such source code further may be compiled into someform of instructions or object code (including assembly languageinstructions or configuration information). The software or source codeof the present invention may be embodied as any type of source code,such as SQL and its variations (e.g., SQL 99 or proprietary versions ofSQL), C, C++, Java, or any other type of programming language whichperforms the functionality discussed above. As a consequence, a“construct” or “program construct”, as used herein, means and refers toany programming language, of any kind, with any syntax or signatures,which provides or can be interpreted to provide the associatedfunctionality or methodology (when instantiated or loaded into a serveror other computing device).

The software or other code of the present invention, such as anyresulting or compiled bit file (object code or configuration bitsequence), may be embodied within any tangible storage medium, such aswithin a memory or storage device for use by a computer, a workstation,any other machine-readable medium or form, or any other storage form ormedium for use in a computing system. Such storage medium, memory orother storage devices may be any type of memory device, memoryintegrated circuit (“IC”), or memory portion of an integrated circuit(such as the resident memory within a processor IC), including withoutlimitation RAM, FLASH, DRAM, SRAM, MRAM, FeRAM, ROM, EPROM or EPROM, orany other type of memory, storage medium, or data storage apparatus orcircuit, depending upon the selected embodiment. For example, withoutlimitation, a tangible medium storing computer readable software, orother machine-readable medium, may include a floppy disk, a CDROM, aCD-RW, a magnetic hard drive, an optical drive, a quantum computingstorage medium or device, a transmitted electromagnetic signal (e.g.,used in internet downloading), or any other type of data storageapparatus or medium.

The results, information and other data provided by the system, methodsand programs of the present invention also may be embodied as a datastructure and stored or provided in any number of forms and media, suchas a data structure stored within any type of computer, within aworkstation, within an application server such as application server125, within a database management server 140, within a computer network,within a database 100, within an adaptive computing device, or withinany form of memory, storage device, or machine-readable media, asdiscussed above. In accordance with the present invention, such a datastructure is comprised of at least two fields of a plurality of fields,as follows.

A first field of the plurality of fields provides or stores information,such as codes or designations, pertaining to a first plurality ofclassifications which provide identification of persons according to aselected property. For example, the plurality of predictive attitudinalclassifications identify persons (in either or both the referencepopulation or the selected population), according to an attitudinal(selected) property. In other circumstances, this identification of“who” may be based on other selected properties, such as behavioralcharacteristics, demographic characteristics, geographiccharacteristics, financial characteristics, transactionalcharacteristics, etc., such as identification of persons who engage incertain activities, who live in certain types of households, who live ina certain region or postal code area, who have incomes greater than acertain amount, who purchase particular goods of a particular monetaryamount, and so on.

Optionally, depending upon the selected embodiment, additional fieldsrelated to this first field or which are subfields of this first fieldprovide or store additional information pertaining to, for example, thepercentage of the selected population or the reference population withineach classification of the first plurality of classifications, or thecorresponding penetration indices for each classification of the firstplurality of classifications, or both, such as the correspondingpenetration indices for the plurality of predictive attitudinalclassifications. Other information in these additional fields orsubfields may also specify a size of a prospect population, formarketing applications, for example.

Also optionally, depending on the selected embodiment, the first fieldmay also include additional fields or subfields based on other relevantor related properties. For example, this first field may be furtherdivided into categories such as core, niche and growth classifications,as discussed above.

A second field of the plurality of fields provides or storesinformation, such as codes or designations, pertaining to a secondplurality of classifications, in which the second plurality ofclassifications provides information pertaining to a correspondingplurality of message versions, message content, or message themes. Thissecond field providing a designation or code for the “what” of acommunication will typically have one or two forms (or both), such ascontaining general information concerning types of messages, as in theplurality of predictive message content classifications described above,or containing more particular information, such as specific contentversions correspondingly tailored to the plurality of predictive messagecontent classifications. For an example of the latter case, this secondfield may include at least one designation or code for a particularversion (of a plurality of content versions) for use in a direct mail tothe entities identified in the first field (via the plurality ofpredictive attitudinal classifications), with other versions transmittedto other entities of the other classifications of the first plurality ofclassifications.

A third field of the plurality of fields provides or stores information,such as codes or designations, pertaining to a third plurality ofclassifications which provide media/channel information, such as themedia and channel preferences which correspond to the preferences of theindividuals identified in the first field. For example, this third fieldmay include designations or codes (providing the “how” of acommunication) corresponding to communication media (channels), such asfor electronic mail, internet, direct mail, telecommunication, broadcastmedia (such as radio, television, cable, satellite), video media,optical media (DVD, CD), print media (newspaper, weekly magazines,monthly magazines, journals, business reports), electronic media (suchas web sites and electronic forms of newspapers, magazines), and publicdisplay media (such as signage, billboards, multimedia displays).

A fourth field of the plurality of fields provides or storesinformation, such as codes or designations, pertaining to a fourthplurality of classifications which provide communication timinginformation. For example, this fourth field may include designations orcodes (providing the “when” of a communication) corresponding tocommunication timing classifications such as morning, afternoon,evening, night, weekday, weekend, any time (no preference), and none.

A fifth field of the plurality of fields provides or stores information,such as codes or designations, pertaining to a fifth plurality ofclassifications which provide frequency of communication information.For example, this fifth field may include designations or codescorresponding to predictive communication frequency classifications,such as daily, weekly, biweekly, monthly, semi-monthly, bimonthly,annually, semi-annually, and none.

A sixth field of the plurality of fields provides or stores information,such as codes or designations, pertaining to a sixth plurality ofclassifications which provide communication sequencing information. Forexample, this fourth field may include designations or codescorresponding to particular sequences of communications, such as directmail, followed by electronic media, followed by email. As indicatedabove, there are innumerable such combinations available.

A wide variety of selections of which fields are included in the datastructure and the ordering of these various selected fields areavailable, as will be apparent to those of skill in the art, and arewithin the scope of the invention. In addition, this data structureembodiment may be housed, embodied, or stored in myriad orders andlocations, such as different memory locations as directed by a DMAengine or memory address generator, for example. The data structure ofthe present invention may also be embodied, stored, distributed orcommunicated in a wide variety of forms, such as electronically (e.g.,internet, wireless, email, storage disk), or through various printmedia, for example, such as in the form of a market research report.

In summary, the present invention provides a method, system and softwarefor independently predicting a plurality of attitudinal classificationsand a plurality of message content classifications, for a selectedpopulation of a plurality of entities (such as individuals orhouseholds) represented in a data repository. The method, system andsoftware embodiments of the invention, in operation, each perform thefollowing:

-   -   First, for each entity of the plurality of entities of the        selected population, appending from the data repository a        corresponding predictive attitudinal classification of a        plurality of predictive attitudinal classifications and a        corresponding plurality of predictive message content        classifications, with the corresponding predictive attitudinal        classification and corresponding plurality of predictive message        content classifications having been determined using a plurality        of predictive attitudinal models developed from a sample        population and applied to a reference population represented in        the data repository.    -   Second, for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications, determining        a penetration index of the selected population compared to the        reference population; and    -   Third, for each predictive attitudinal classification of the        plurality of predictive attitudinal classifications,        independently determining at least one predominant predictive        message content classification from the appended plurality of        predictive message content classifications of the plurality of        entities of the selected population having the corresponding        predictive attitudinal classification of the plurality of        predictive attitudinal classifications.

Typically, the independent determination of at least one predominantpredictive message content classification comprises: for each predictiveattitudinal classification, determining all of the appended plurality ofpredictive message content classifications of the plurality of entitiesof the selected population having the corresponding predictiveattitudinal classification; and selecting one or more predictive messagecontent classifications corresponding to a comparatively greater numberof entities of the selected population.

In addition, depending upon the selected embodiment, for each entity ofthe plurality of entities of the selected population, the variousembodiments provide for appending from the data repository at least onecorresponding predictive communication media/channel classification of aplurality of predictive communication media classifications, with thecorresponding predictive communication media classification having beendetermined from information stored in the data repository. For eachpredictive attitudinal classification of the plurality of predictiveattitudinal classifications, the various embodiments provide forindependently determining at least one predominant predictivecommunication media classification from the appended plurality ofpredictive communication media classifications of the plurality ofentities of the selected population having the corresponding predictiveattitudinal classification of the plurality of predictive attitudinalclassifications. Typically, the plurality of predictive communicationmedia classifications comprises at least two of the followingcommunication media: electronic mail (email), direct mail,telecommunication, radio, television, video or DVD (digital versatiledisk) media, print media, and visual or public display media. Dependingupon the selected embodiment, the plurality of communication and mediachannel classifications may be more or less specific, such as furthersubdividing print and electronic media channels into newspaper, weeklymagazines, monthly magazines, journals, business reports, and furtherinto their print, internet, email or electronic versions, and such asfurther subdividing broadcast media such as radio and television intonetwork, cable, satellite, etc.

Similarly, depending upon the selected embodiment, for each entity ofthe plurality of entities of the selected population, the variousembodiments provide for appending from the data repository at least onecorresponding predictive communication timing classification of aplurality of predictive communication timing classifications, thecorresponding predictive communication timing classification having beendetermined from information stored in the data repository. For eachpredictive attitudinal classification of the plurality of predictiveattitudinal classifications, the various embodiments provide forindependently determining at least one predominant predictivecommunication timing classification from the appended plurality ofpredictive communication timing classifications of the plurality ofentities of the selected population having the corresponding predictiveattitudinal classification of the plurality of predictive attitudinalclassifications. Also typically, the plurality of predictivecommunication timing classifications comprises at least two of thefollowing communication timing classifications: any time, morning,afternoon, evening, night, weekday, and weekend.

Also similarly, depending upon the selected embodiment, for each entityof the plurality of entities of the selected population, the variousembodiments provide for appending from the data repository at least onecorresponding predictive communication frequency classification of aplurality of predictive communication frequency classifications, thecorresponding predictive communication frequency classification havingbeen determined from information stored in the data repository. For eachpredictive attitudinal classification of the plurality of predictiveattitudinal classifications, the various embodiments provide forindependently determining at least one predominant predictivecommunication frequency classification from the appended plurality ofpredictive communication frequency classifications of the plurality ofentities of the selected population having the corresponding predictiveattitudinal classification of the plurality of predictive attitudinalclassifications. The plurality of predictive communication frequencyclassifications typically comprises at least two of the followingfrequency classifications: daily, weekly, biweekly, monthly,semi-monthly, bimonthly, annually, semi-annually, unlimited, and none.

Also similarly, depending upon the selected embodiment, for each entityof the plurality of entities of the selected population, the variousembodiments provide for appending from the data repository at least onecorresponding predictive communication sequencing classification of aplurality of predictive communication sequencing classifications, thecorresponding predictive communication sequencing classification havingbeen determined from information stored in the data repository. For eachpredictive attitudinal classification of the plurality of predictiveattitudinal classifications, the various embodiments provide forindependently determining at least one predominant predictivecommunication sequencing classification from the appended plurality ofpredictive communication sequencing classifications of the plurality ofentities of the selected population having the corresponding predictiveattitudinal classification of the plurality of predictive attitudinalclassifications.

As part of the present invention, the various embodiments provide fordetermining the selected population of the plurality of entities bymatching a listing of a plurality of customers to the referencepopulation represented in the data repository. Alternatively,non-matching entities of the selected population may simply beconsidered eliminated from the processes involving the selectedpopulation. Exclusion of entities of the selected population from theseprocesses may also be dependent upon a level or degree of match to theentities of the data repository 100, such as matching to an individual,a household, or merely a geographic or postal code area.

Also in summary, in the various embodiments, the plurality of predictivemessage content classifications are determined by: developing aplurality of empirical attitudinal factors based on a factor analysis ofan attitudinal survey of the sample population; using each empiricalattitudinal factor of the plurality of empirical attitudinal factors,scoring each participant of the attitudinal survey to create acorresponding plurality of empirical attitudinal factor scores; using aplurality of selected variables from the data repository as independentvariables, and using the corresponding plurality of empiricalattitudinal factor scores as dependent variables, performing aregression analysis to create the plurality of predictive attitudinalmodels; and using each predictive attitudinal model of the plurality ofpredictive attitudinal models, scoring the plurality of entitiesrepresented in the data repository, as the reference population, tocreate the plurality of predictive message content classifications. Theplurality of predictive attitudinal classifications are determined by acluster analysis of the plurality of predictive message contentclassifications of each entity of the plurality of entities representedin the data repository.

The invention also provides for determining core, niche and growthattitudinal classifications, as follows: determining one or more coreattitudinal classifications by selecting, from the plurality ofpredictive attitudinal classifications, at least one predictiveattitudinal classification having a comparatively greater (e.g., averageor above average) penetration index and having a comparatively greaterproportion of the selected population; determining one or more nicheattitudinal classifications by selecting, from the plurality ofpredictive attitudinal classifications, at least one predictiveattitudinal classification having a comparatively greater penetrationindex and having a comparatively lesser proportion of the referencepopulation; and determining one or more growth attitudinalclassifications by selecting, from the plurality of predictiveattitudinal classifications, at least one predictive attitudinalclassification having a comparatively lesser (e.g., below average)penetration index and having a comparatively greater proportion of thereference population.

Numerous advantages of the present invention are readily apparent. Theembodiments of the present invention provide a predictive methodology,system and software, for accurate prediction of attitudes, motivationsand behaviors, which may be utilized for marketing, research,assessment, and other applications. The embodiments of the invention areempirically-based upon actual attitudinal research and other informationfrom a population sample, and provide accurate modeling to predict andextrapolate such attitudinal information to a larger referencepopulation. In addition to identifying to “whom” a communication shouldbe directed, the embodiments of the invention further provideinformation concerning the “what” of the communication, such as thepreferred message themes or message content, independently from anypopulation grouping, segmentation or clustering process. In addition,the embodiments of the invention provide actionable results, providingnot only audience attitudinal information and preferred message content,but also preferred communication channel or media information,communication frequency, communication timing information, andcommunication sequencing information.

From the foregoing, it will be observed that numerous variations andmodifications may be effected without departing from the spirit andscope of the novel concept of the invention. It is to be understood thatno limitation with respect to the specific methods and apparatusillustrated herein is intended or should be inferred. It is, of course,intended to cover by the appended claims all such modifications as fallwithin the scope of the claims.

It is claimed:
 1. A method of determining a plurality of predictivemessage content classifications and a plurality of predictiveattitudinal classifications for a reference population represented in adata repository, the method comprising: developing a plurality ofempirical attitudinal factors based on a factor analysis of anattitudinal survey of a sample population; using each empiricalattitudinal factor of the plurality of empirical attitudinal factors,scoring each participant of the attitudinal survey to create acorresponding plurality of empirical attitudinal factor scores; using aplurality of selected variables from the data repository as independentvariables, and using the corresponding plurality of empiricalattitudinal factor scores as dependent variables, performing aregression analysis to create a plurality of predictive attitudinalmodels; using each predictive attitudinal model of the plurality ofpredictive attitudinal models, scoring a plurality of entities formingthe reference population represented in the data repository, to createthe plurality of predictive message content classifications; andperforming a cluster analysis of the plurality of predictive messagecontent classifications of each entity of the plurality of entitiesforming the reference population represented in the data repository tocreate the plurality of predictive attitudinal classifications.
 2. Themethod of claim 1, further comprising: validating the plurality ofpredictive attitudinal models using a subset of the sample populationand corresponding results of the attitudinal survey.
 3. The method ofclaim 1, wherein the cluster analysis includes determining a pluralityof combinations of membership and non-membership of the plurality ofentities in each of the plurality of predictive message contentclassifications.
 4. The method of claim 1, wherein the cluster analysisincludes determining a plurality of combinations of probabilities ofmembership of the plurality of entities in each of the plurality ofpredictive message content classifications.
 5. The method of claim 1,wherein the cluster analysis includes determining a plurality ofcombinations of probabilities of the plurality of entities exhibiting anattitude represented by each of the plurality of predictive messagecontent classifications.
 6. The method of claim 1, wherein theregression analysis is a logistic regression analysis.
 7. The method ofclaim 1, further comprising: independently determining a subset of theplurality of predictive attitudinal classifications and a subset of theplurality of predictive message content classifications for a selectedpopulation of a plurality of entities represented in a data repository.8. The method of claim 7, wherein the independent determination of thesubset of the plurality of predictive attitudinal classifications andthe subset of the plurality of predictive message contentclassifications further comprises: for each entity of the plurality ofentities of the selected population, appending from the data repositorya corresponding predictive attitudinal classification of the pluralityof predictive attitudinal classifications and a corresponding pluralityof memberships in the plurality of predictive message contentclassifications; for each predictive attitudinal classification of theplurality of predictive attitudinal classifications, determining apenetration index of the selected population compared to the referencepopulation; and for each predictive attitudinal classification of theplurality of predictive attitudinal classifications, independentlydetermining at least one predominant predictive message contentclassification from the appended corresponding plurality of membershipsin the plurality of predictive message content classifications of theplurality of entities of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 9. The method of claim 8,further comprising: for each entity of the plurality of entities of theselected population, appending from the data repository at least onecorresponding predictive communication media classification of aplurality of predictive communication media classifications, thecorresponding predictive communication media classification having beendetermined from information stored in the data repository; and for eachpredictive attitudinal classification of the plurality of predictiveattitudinal classifications, independently determining at least onepredominant predictive communication media classification from theappended plurality of predictive communication media classifications ofthe plurality of individuals of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 10. The method of claim 9,wherein the plurality of predictive communication media classificationscomprises at least two of the following: electronic mail (email), directmail, telecommunication, radio, television, internet, satellite, cablemedia, video media, digital versatile disk media, compact disk media,print media, and visual display media.
 11. The method of claim 8,further comprising: for each entity of the plurality of entities of theselected population, appending from the data repository at least onecorresponding predictive communication timing classification of aplurality of predictive communication timing classifications, thecorresponding predictive communication timing classification having beendetermined from information stored in the data repository; and for eachpredictive attitudinal classification of the plurality of predictiveattitudinal classifications, independently determining at least onepredominant predictive communication timing classification from theappended plurality of predictive communication timing classifications ofthe plurality of entities of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 12. The method of claim 11,wherein the plurality of predictive communication timing classificationscomprises at least two of the following communication timingclassifications: any time, morning, afternoon, evening, night, weekday,and weekend.
 13. The method of claim 8, further comprising: for eachentity of the plurality of entities of the selected population,appending from the data repository at least one corresponding predictivecommunication frequency classification of a plurality of predictivecommunication frequency classifications, the corresponding predictivecommunication frequency classification having been determined frominformation stored in the data repository; and for each predictiveattitudinal classification of the plurality of predictive attitudinalclassifications, independently determining at least one predominantpredictive communication frequency classification from the appendedplurality of predictive communication frequency classifications of theplurality of entities of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 14. The method of claim 13,wherein the plurality of predictive communication frequencyclassifications comprises at least two of the following frequencyclassifications: unlimited, none, daily, weekly, biweekly, monthly,semi-monthly, bimonthly, annually, and semi-annually.
 15. The method ofclaim 8, further comprising: determining at least one core attitudinalclassifications by selecting, from the plurality of predictiveattitudinal classifications, at least one predictive attitudinalclassification having a comparatively greater penetration index andhaving a comparatively greater proportion of the reference population;determining at least one niche attitudinal classifications by selecting,from the plurality of predictive attitudinal classifications, at leastone predictive attitudinal classification having a comparatively greaterpenetration index and having a comparatively lesser proportion of thereference population; and determining at least one growth attitudinalclassifications by selecting, from the plurality of predictiveattitudinal classifications, at least one predictive attitudinalclassification having a comparatively lesser penetration index andhaving a comparatively greater proportion of the reference population.16. The method of claim 8, wherein the independent determination of atleast one predominant predictive message content classification furthercomprises: for each predictive attitudinal classification, determiningall of the appended corresponding plurality of memberships in theplurality of predictive message content classifications of the pluralityof entities of the selected population having the correspondingpredictive attitudinal classification; and for each predictiveattitudinal classification, selecting at least one predictive messagecontent classifications, of the plurality of predictive message contentclassifications, corresponding to a comparatively greater number ofentities of the selected population.
 17. A system for determining aplurality of predictive message content classifications and a pluralityof predictive attitudinal classifications for a reference population,the system comprising: a data repository storing a plurality of selectedvariables for each entity of a plurality of entities forming a samplepopulation and forming a reference population; and a processor coupledto the data repository, the processor configured to perform a factoranalysis of an attitudinal survey of the sample population to determinea plurality of empirical attitudinal factors; to score each participantof the attitudinal survey, using each empirical attitudinal factor ofthe plurality of empirical attitudinal factors, to create acorresponding plurality of empirical attitudinal factor scores; toperform a regression analysis using the plurality of selected variablesfrom the data repository as independent variables, and using thecorresponding plurality of empirical attitudinal factor scores asdependent variables, to create a plurality of predictive attitudinalmodels; to score the plurality of entities forming the referencepopulation using each predictive attitudinal model of the plurality ofpredictive attitudinal models, to create the plurality of predictivemessage content classifications; and to perform a cluster analysis ofthe plurality of predictive message content classifications of eachentity of the plurality of entities forming the reference population, tocreate the plurality of predictive attitudinal classifications.
 18. Thesystem of claim 17, wherein the processor is further configured tovalidate the plurality of predictive attitudinal models using a subsetof the sample population and corresponding results of the attitudinalsurvey.
 19. The system of claim 17, wherein the processor is furtherconfigured to perform the cluster analysis by determining a plurality ofcombinations of membership and non-membership of the plurality ofentities in each of the plurality of predictive message contentclassifications.
 20. The system of claim 17, wherein the processor isfurther configured to perform the cluster analysis by determining aplurality of combinations of probabilities of membership of theplurality of entities in each of the plurality of predictive messagecontent classifications.
 21. The system of claim 17, wherein theprocessor is further configured to perform the cluster analysis bydetermining a plurality of combinations of probabilities of theplurality of entities exhibiting an attitude represented by each of theplurality of predictive message content classifications.
 22. The systemof claim 17, wherein the processor is further configured to perform theregression analysis as a logistic regression analysis.
 23. The system ofclaim 17, wherein the processor is further configured to store, in thedata repository, for each entity of the plurality of entities of thereference population, a corresponding predictive attitudinalclassification of the plurality of predictive attitudinalclassifications and a corresponding plurality of memberships in theplurality of predictive message content classifications determined froma corresponding plurality of scores from the plurality of predictiveattitudinal models.
 24. The system of claim 23, wherein the processor isfurther configured to independently determine a subset of the pluralityof predictive attitudinal classifications and a subset of the pluralityof predictive message content classifications for the selectedpopulation.
 25. The system of claim 58, wherein the processor is furtherconfigured to append from the data repository, for each entity of theplurality of entities of the selected population, the correspondingpredictive attitudinal classification of the plurality of predictiveattitudinal classifications and the corresponding plurality ofmemberships in the plurality of predictive message contentclassifications; for each predictive attitudinal classification of theplurality of predictive attitudinal classifications, to determine apenetration index of the selected population compared to the referencepopulation; and for each predictive attitudinal classification of theplurality of predictive attitudinal classifications, to independentlydetermine at least one predominant predictive message contentclassification from the appended corresponding plurality of membershipsin the plurality of predictive message content classifications of theplurality of entities of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 26. The system of claim 25,wherein the processor is further configured to append from the datarepository, for each entity of the plurality of entities of the selectedpopulation, at least one corresponding predictive communication mediaclassification of a plurality of predictive communication mediaclassifications, the corresponding predictive communication mediaclassification having been determined by the processor from informationstored in the data repository; and for each predictive attitudinalclassification of the plurality of predictive attitudinalclassifications, to independently determine at least one predominantpredictive communication media classification from the appendedplurality of predictive communication media classifications of theplurality of individuals of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 27. The system of claim 26,wherein the plurality of predictive communication media classificationscomprises at least two of the following: electronic mail (email), directmail, telecommunication, radio, television, internet, video media,digital versatile disk media, print media, and visual display media. 28.The system of claim 25, wherein the processor is further configured toappend from the data repository, for each entity of the plurality ofentities of the selected population, at least one correspondingpredictive communication timing classification of a plurality ofpredictive communication timing classifications, the correspondingpredictive communication timing classification having been determinedfrom information stored in the data repository; and for each predictiveattitudinal classification of the plurality of predictive attitudinalclassifications, to independently determine at least one predominantpredictive communication timing classification from the appendedplurality of predictive communication timing classifications of theplurality of entities of the selected population having thecorresponding predictive attitudinal classification of the plurality ofpredictive attitudinal classifications.
 29. The system of claim 28,wherein the plurality of predictive communication timing classificationscomprises at least two of the following communication timingclassifications: any time, morning, afternoon, evening, night, weekday,and weekend.
 30. The system of claim 25, wherein the processor isfurther configured to append from the data repository, for each entityof the plurality of entities of the selected population, at least onecorresponding predictive communication frequency classification of aplurality of predictive communication frequency classifications, thecorresponding predictive communication frequency classification havingbeen determined by the processor from information stored in the datarepository; and for each predictive attitudinal classification of theplurality of predictive attitudinal classifications, to independentlydetermine at least one predominant predictive communication frequencyclassification from the appended plurality of predictive communicationfrequency classifications of the plurality of entities of the selectedpopulation having the corresponding predictive attitudinalclassification of the plurality of predictive attitudinalclassifications.
 31. The system of claim 30, wherein the plurality ofpredictive communication frequency classifications comprises at leasttwo of the following frequency classifications: unlimited, none, daily,weekly, biweekly, monthly, semi-monthly, bimonthly, annually, andsemi-annually.
 32. The system of claim 25, wherein the processor isfurther configured to determine at least one core attitudinalclassifications by selecting, from the plurality of predictiveattitudinal classifications, at least one predictive attitudinalclassification having a comparatively greater penetration index andhaving a comparatively greater proportion of the reference population;to determine at least one niche attitudinal classifications byselecting, from the plurality of predictive attitudinal classifications,at least one predictive attitudinal classification having acomparatively greater penetration index and having a comparativelylesser proportion of the reference population; and to determine at leastone growth attitudinal classifications by selecting, from the pluralityof predictive attitudinal classifications, at least one predictiveattitudinal classification having a comparatively lesser penetrationindex and having a comparatively greater proportion of the referencepopulation.
 33. The system of claim 25, wherein the processor is furtherconfigured to independently determine the at least one predominantpredictive message content classification by determining, for eachpredictive attitudinal classification, all of the appended correspondingplurality of memberships in the plurality of predictive message contentclassifications of the plurality of entities of the selected populationhaving the corresponding predictive attitudinal classification; and byselecting, for each predictive attitudinal classification, at least onepredictive message content classifications, of the plurality ofpredictive message content classifications, corresponding to acomparatively greater number of entities of the selected population.